Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning

Zhang, X; Fang, Y; Liu, Q; Yazdani, D

Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning

Zhang, X Fang, Y Liu, Q Yazdani, D

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Publisher:: KOREAN SOC PRECISION ENG
Publication Type:: Journal Article
Citation:: International Journal of Precision Engineering and Manufacturing, 2024, 25, (1), pp. 111-130
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhang, X
dc.contributor.author	Fang, Y
dc.contributor.author	Liu, Q
dc.contributor.author	Yazdani, D https://orcid.org/0000-0002-7799-5013
dc.date.accessioned	2024-04-01T09:04:51Z
dc.date.available	2024-04-01T09:04:51Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Precision Engineering and Manufacturing, 2024, 25, (1), pp. 111-130
dc.identifier.issn	2234-7593
dc.identifier.issn	2005-4602
dc.identifier.uri	http://hdl.handle.net/10453/177393
dc.description.abstract	Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.
dc.language	English
dc.publisher	KOREAN SOC PRECISION ENG
dc.relation.ispartof	International Journal of Precision Engineering and Manufacturing
dc.relation.isbasedon	10.1007/s12541-023-00900-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning
dc.type	Journal Article
utslib.citation.volume	25
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-01T09:04:48Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	1

Abstract:

Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.

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http://hdl.handle.net/10453/177393